Daily air pollution has been linked with mortality from urban studies. Associations in rural areas are still unclear and there is growing interest in testing the role that air pollution has on other causes of death. This study aims to evaluate the association between daily air pollution and cause-specific mortality in all 8092 Italian municipalities. Natural, cardiovascular, cardiac, ischemic, cerebrovascular, respiratory, metabolic, diabetes, nervous and psychiatric causes of death occurred in Italy were extracted during 2013-2015. Daily ambient PM₁₀, PM_2.5 and NO₂ concentrations were estimated through machine learning algorithms. The associations between air pollutants and cause-specific mortality were estimated with a time-series approach using a two-stage analytic protocol where area-specific over-dispersed Poisson regression models where fit in the first stage, followed by a meta-analysis in the second. We tested for effect modification by sex, age class and the degree of urbanisation of the municipality. We estimated a positive association between PM₁₀ and PM_2.5 and the mortality from natural, cardiovascular, cardiac, respiratory and nervous system causes, but not with metabolic or psychiatric causes of death. In particular, mortality from nervous diseases increased by 4.55% (95% CI: 2.51-6.63) and 9.64% (95% CI: 5.76-13.65) for increments of 10 μg/m³ in PM₁₀ and PM_2.5 (lag 0-5 days), respectively. NO₂ was positively associated with respiratory (6.68% (95% CI: 1.04-12.62)) and metabolic (7.30% (95% CI: 1.03-13.95)) mortality for increments of 10 μg/m³ (lag 0-5). Higher associations with natural mortality were found among the elderly, while there were no differential effects between sex or between rural and urban areas. Short-term exposure to particulate matter was associated with mortality from nervous diseases. Mortality from metabolic diseases was associated with NO₂ exposure. Other associations are confirmed and updated, including the contribution of lowly urbanised areas. Health effects were also found in suburban and rural areas.

This is the first study that quantifies explicitly the impact of present vegetation on concentrations and depositions, considering simultaneously its effects on meteorology, biogenic emissions, dispersion, and dry deposition in three European cities: Bologna, Milan, and Madrid. The behaviour of three pollutants (O₃, NO_2, and PM10) was investigated considering two different scenarios, with the actual vegetation (VEG) and without it (NOVEG) for two months, representative of summer and winter seasons: July and January. The evaluation is based on simulations performed with two state-of-the-art atmospheric modelling systems (AMS) that use similar but not identical descriptions of physical and chemical atmospheric processes: AMS-MINNI for the two Italian cities and WRF-CMAQ for the Spanish city. The choice of using two AMS and applying one of them in two cities has been made to ensure the robustness of the results needed for their further generalization. The analysis of the spatial distribution of the vegetation effects on air concentrations and depositions shows that they are highly variable from one grid cell to another in the city area, with positive/negative effects or high/low effects in adjacent cells being observed for the three pollutants investigated in all cities. According to the pollutant, on a monthly basis, the highest differences in concentrations (VEG-NOVEG) produced by vegetation were estimated in July for O₃ (−7.40 μg/m³ in Madrid and +2.67 μg/m³ in Milan) and NO₂ (−3.01 μg/m³ in Milan and +7.17 μg/m³ in Madrid) and in January for PM10 (−3.14 μg/m³ in Milan +2.01 μg/m³ in Madrid). Thus, in some parts of the cities, the presence of vegetation had produced an increase in pollutant concentrations despite its efficient removal action that ranges from ca. 17% for O₃ in Bologna (January) to ca. 77% for NO₂ in Madrid (July).

Air pollution effects on cardiovascular hospitalizations in small urban/suburban areas have been scantly investigated. Such effects were assessed among the participants in the analytical epidemiological survey carried out in Pisa and Cascina, Tuscany, Italy (2009–2011). Cardiovascular hospitalizations from 1585 subjects were followed up (2011–2015). Daily mean pollutant concentrations were estimated through random forests at 1 km (particulate matter: PM₁₀, 2011–2015; PM_2.5, 2013–2015) and 200 m (PM₁₀, PM_2.5, NO₂, O₃, 2013–2015) resolutions. Exposure effects were estimated using the case-crossover design and conditional logistic regression (odds ratio—OR—and 95% confidence interval—CI—for 10 μg/m³ increase; lag 0–6). During the period 2011–2015 (137 hospitalizations), a significant effect at lag 0 was observed for PM₁₀ (OR = 1.137, CI: 1.023–1.264) at 1 km resolution. During the period 2013–2015 (69 hospitalizations), significant effects at lag 0 were observed for PM₁₀ (OR = 1.268, CI: 1.085–1.483) and PM_2.5 (OR = 1.273, CI: 1.053–1.540) at 1 km resolution, as well as for PM₁₀ (OR = 1.365, CI: 1.103–1.690), PM_2.5 (OR = 1.264, CI: 1.006–1.589) and NO₂ (OR = 1.477, CI: 1.058–2.061) at 200 m resolution; significant effects were observed up to lag 2. Larger ORs were observed in males and in subjects reporting pre-existent cardiovascular/respiratory diseases. Combining analytical and routine epidemiological data with high-resolution pollutant estimates provides new insights on acute cardiovascular effects in the general population and in potentially susceptible subgroups living in small urban/suburban areas.

This study quantifies the vegetation impact on urban meteorology by means of the numerical model WRF (Weather Research and Forecasting model). The assessment was made for two months: July and January. These were considered as representative for the summer and winter seasons, for the reference year 2015 in three European cities: Bologna, Milano, and Madrid. Two simulations at 1 km resolution were conducted over the cities with and without the actual urban vegetation, called VEG and NOVEG, respectively, in the model input. Then, the impact of vegetation was evaluated as the difference between the two simulations (VEG-NOVEG) for temperature, relative humidity, and wind speed fields. In general, we found that, as can be expected, urban vegetation tends to cool the atmosphere, enhance the humidity, and reduce the wind speed. However, in some cases, areas with the opposite behaviour exist, so that no a priori results can be attributed to the presence of urban vegetation. Moreover, even when major impact is confined around grid cells where urban vegetation is present, changes in meteorological quantities can be observed elsewhere in the city’s area. The magnitude of urban vegetation impact is higher in summer than in winter and it depends on the city’s morphological peculiarities, such as urban texture and vegetation types and distribution: average July temperature variations due to the presence of urban vegetation reach peaks of −0.8 °C in Milano, −0.6 °C Madrid, and −0.4 °C in Bologna, while in January, the values range between −0.3 and −0.1 °C. An average heating effect of ca. +0.2 °C is found in some parts of Madrid in January. For relative humidity, we found increments of 2%–3% in July and 0.5%–0.8% in January, while a decrease in wind speed was found between 0.1 and 0.5 m/s, with the highest occurring in Madrid during July.

The role of urban vegetation in urban air quality is usually assessed by considering only the pollutant removal capacity of the plants. This study aims to show, for the first time, the effects of vegetation on air pollutant concentrations through its effects on meteorology, separately from its biogenic emissions. It also investigates how air quality changes when only biogenic emissions are altered by using plants with different emission factors, as well as the potential effects of introducing new vegetation into urban areas. These assessments were conducted using atmospheric modelling systems currently employed for air quality forecasting and planning, configured specifically for the cities of Bologna and Milan. Simulations were performed for two representative months, July and January, to capture summer and winter conditions, respectively. The variability in air concentrations of ozone (O₃), nitrogen dioxide (NO₂), and particulate matter (PM10) within the municipal boundaries was assessed monthly. When evaluating the impact of future vegetation, changes in temperature, wind speed, and relative humidity were also considered. The results indicate that vegetation influences air quality more significantly through changes in meteorological conditions than through biogenic emissions. Changes in biogenic emissions result in similar behaviours in O₃ and PM10 concentrations, with the latter being affected by the changes in the concentrations of secondary biogenic aerosols formed in the atmosphere. Changes in NO₂ concentrations are controlled by the changes in O₃ concentrations, increasing where O₃ concentrations decrease, and vice versa, as expected in highly polluted areas. Meteorologically induced vegetation effects also play a predominant role in depositions, accounting for most of the changes; however, the concentrations remain high despite increased deposition rates. Therefore, understanding only the removal characteristics of vegetation is insufficient to quantify its effects on urban air pollution.